We quantify a recent five-category CT histogram based classification of ground glass opacities using a dynamic mathematical model for the spatial-temporal evolution of malignant nodules. Our mathematical model takes the form of a spatially structured partial differential equation with a logistic crowding term. We present the results of extensive simulations and validate our model using patient data obtained from clinical CT images from patients with benign and malignant lesions.

Figure 1.
Photomicrograph showing a small lung area at the microscopic level. Lighter pink areas are representing the thickened alveolar walls and the darker purple ones are cancer cells lining up along the walls. As the tumor grows further, it will fill the white air spaces between the alveolar walls, thereby shifting the density histogram closer to water

Figure 22.
Total tumor mass growth curves from model simulations. Black dots are time points corresponding to CT scan data for patients 1, 2, 3, 4. Red dots are for two additional time points for Patients 3 and 4. The values are scaled to 1.0 at time 0